AI Threat Alert indexes 4,319+ peer-reviewed and preprint papers on AI/ML security — covering adversarial attacks, model defenses, red-teaming benchmarks, surveys, and security tooling. Papers are sourced from arXiv, classified by type and by relevance to real-world threats, and cross-referenced with the CVEs and incidents they relate to.
Showing 3401–3420 of 4,319 papers
Xin Yang, Omid Ardakanian
Data obfuscation is a promising technique for mitigating attribute inference attacks by semi-trusted parties with access to time-series data emitted...
Peichun Hua, Hao Li, Shanghao Shi +2 more
Large Vision-Language Models (LVLMs) are vulnerable to a growing array of multimodal jailbreak attacks, necessitating defenses that are both...
Jie Ma, Junqing Zhang, Guanxiong Shen +2 more
Radio frequency fingerprint identification (RFFI) is an emerging technique for the lightweight authentication of wireless Internet of things (IoT)...
Edward Lue Chee Lip, Anthony Channg, Diana Kim +2 more
As AI capabilities advance, we increasingly rely on powerful models to decompose complex tasks $\unicode{x2013}$ but what if the decomposer itself is...
Andrew Adiletta, Kathryn Adiletta, Kemal Derya +1 more
The rapid deployment of Large Language Models (LLMs) has created an urgent need for enhanced security and privacy measures in Machine Learning (ML)....
Jamal Al-Karaki, Muhammad Al-Zafar Khan, Rand Derar Mohammad Al Athamneh
The scarcity of cyberattack data hinders the development of robust intrusion detection systems. This paper introduces PHANTOM, a novel adversarial...
Jing Cui, Yufei Han, Jianbin Jiao +1 more
Backdoor attacks embed malicious behaviors into Large Language Models (LLMs), enabling adversaries to trigger harmful outputs or bypass safety...
Alexander K. Saeri, Sophia Lloyd George, Jess Graham +4 more
Organizations and governments that develop, deploy, use, and govern AI must coordinate on effective risk mitigation. However, the landscape of AI...
Manon Kempermann, Sai Suresh Macharla Vasu, Mahalakshmi Raveenthiran +2 more
Safety evaluations of large language models (LLMs) typically focus on universal risks like dangerous capabilities or undesirable propensities....
Neha, Tarunpreet Bhatia
Intrusion Detection Systems (IDS) are critical components in safeguarding 5G/6G networks from both internal and external cyber threats. While...
Han Yang, Shaofeng Li, Tian Dong +3 more
Deep Neural Networks (DNNs), as valuable intellectual property, face unauthorized use. Existing protections, such as digital watermarking, are...
Chaomeng Lu, Bert Lagaisse
Vulnerability detection methods based on deep learning (DL) have shown strong performance on benchmark datasets, yet their real-world effectiveness...
Devanshu Sahoo, Manish Prasad, Vasudev Majhi +5 more
Driven by surging submission volumes, scientific peer review has catalyzed two parallel trends: individual over-reliance on LLMs and institutional...
N Mangala, Murtaza Rangwala, S Aishwarya +5 more
Healthcare has become exceptionally sophisticated, as wearables and connected medical devices are revolutionising remote patient monitoring,...
Devanshu Sahoo, Vasudev Majhi, Arjun Neekhra +3 more
The use of Large Language Models (LLMs) as automatic judges for code evaluation is becoming increasingly prevalent in academic environments. But...
Yash Srivastava, Shalin Jain, Sneha Awathare +1 more
The rising demand for collaborative machine learning and data analytics calls for secure and decentralized data sharing frameworks that balance...
Najmul Hasan, Prashanth BusiReddyGari, Haitao Zhao +3 more
Email phishing is one of the most prevalent and globally consequential vectors of cyber intrusion. As systems increasingly deploy Large Language...
Khurram Khalil, Khaza Anuarul Hoque
Generative Artificial Intelligence models, such as Large Language Models (LLMs) and Large Vision Models (VLMs), exhibit state-of-the-art performance...
Mohamed Afane, Abhishek Satyam, Ke Chen +3 more
Backdoor attacks create significant security threats to language models by embedding hidden triggers that manipulate model behavior during inference,...
Jan Betley, Jorio Cocola, Dylan Feng +4 more
LLMs are useful because they generalize so well. But can you have too much of a good thing? We show that a small amount of finetuning in narrow...
AI security research studies how AI and machine-learning systems can be attacked and defended — covering adversarial examples, prompt injection, model poisoning, training-data extraction, and the mitigations against them. AI Threat Alert curates this research from academic sources so security teams can track the threats behind emerging AI risks.
AI Threat Alert indexes 4,319+ papers on AI/ML security, classified across attack, defense, benchmark, survey, and tool categories and updated continuously.
Papers are sourced from arXiv, then classified by type and by relevance to real-world AI/ML threats, and cross-referenced with the CVEs and incidents they relate to.
Coverage spans adversarial attacks, model and system defenses, red-teaming benchmarks, literature surveys, and security tooling for LLMs, ML libraries, AI agents, and inference pipelines.
Every paper is filtered for AI security relevance and linked to the vulnerabilities, vendors, and incidents it relates to, so the research connects directly to operational threat intelligence.
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